جست‌وجوی استاد و پژوهشگر

Anand Vidyashankarمشاهده پروفایل

استاد

• Biostatistics
• Statistics
• Biotechnology
• +۱۰ مورد دیگر

Anand Vidyashankar is a Professor in the Department of Statistics at George Mason University (Fairfax, VA, USA). He has held academic positions at the University of Illinois at Chicago, University of Georgia, and Cornell University. Previously, he worked in pharmaceutical industries including Abbott Labs, Eli Lilly, and held visiting roles at Searle and the University of Wisconsin Madison's Center for Mathematical Sciences. His research focuses on solving scientific problems through mathematical, statistical, and computational methods. Key areas include biostatistics, bioinformatics, robust inference, branching processes, and risk theory, supported by NSF grants and industry collaborations. He co-organized the ICORS-DSSV 2024 conference, integrating robust statistics and data visualization. His work bridges theory and application, addressing challenges in biopharmaceuticals, finance, and internet-based statistical models. Grants include NSF-funded research on statistical inference for random recursive equations (2011–2014).

Muayyad Jabriمشاهده پروفایل

استاد

• Change Management
• Dialogue Processes
• Organizational Behavior
• +۳ مورد دیگر

Muayyad Jabri is an Adjunct Professor at UNE Business School, University of New England, specializing in change management through dialogue and narrative approaches. His research integrates Bakhtin's philosophical concepts with organizational theory, examining change processes in cross-cultural contexts. With visiting professorships in Europe and China, he has developed globally informed perspectives on management practices. Academic Contributions: Author of multiple books including Rethinking Organizational Change: The Role of Dialogue, Dialectic & Polyphony (Routledge) and over 40 refereed articles. His work has been translated into Japanese, German, and Spanish. Professional Expertise: Accredited practitioner in MBTI and KAI instruments. Editorial board member for Journal of Change Management, Journal of Management Development, and Leadership and Organization Development Journal. Consulting Experience: Advised major organizations including Pfizer, ICI Pharmaceuticals, and Amersham International on team design and organizational change.

Pamela E. Harrisمشاهده پروفایل

استاد

• Algebraic Combinatorics
• Representation Theory
• Graph Theory
• +۴ مورد دیگر

Pamela E. Harris is a Professor of Mathematics in the Department of Mathematical Sciences at the University of Wisconsin-Milwaukee. She is also a cohost of the podcast Mathematically Uncensored and President and co-founder of Lathisms: Latinxs and Hispanics in the Mathematical Sciences. Education: PhD in Mathematics (2012), University of Wisconsin-Milwaukee; M.S. in Mathematics (2008), University of Wisconsin-Milwaukee; B.S. in Mathematics (2005), Marquette University; Associate Degrees in Sciences and Liberal Arts (2003), Milwaukee Area Technical College. Her research focuses on algebraic combinatorics, particularly representation theory of Lie algebras, with applications to parking functions, graph theory, and discrete mathematics. She is a leading advocate for diversity, equity, and inclusion in mathematics, mentoring underrepresented students and co-organizing symposia for SACNAS and the American Mathematical Society. Dr. Harris has authored over 65 peer-reviewed articles and coauthored books such as Asked And Answered: Dialogues On Advocating For Students of Color in Mathematics and Practices and Policies: Advocating for Students of Color in Mathematics . Her work includes developing community agreements and DEI frameworks for academic settings. Scientific Awards: MAA Deborah and Franklin Tepper Haimo Award (2023), MAA Northeast Section Award (2020), MAA Henry L. Alder Award (2019), Council on Undergraduate Research Mentor Award (2019), Karen Uhlenbeck EDGE Fellow (2020). She actively engages in promoting community-building initiatives and has contributed to national dialogues on mentorship, equity, and the intersection of mathematics with social justice.

• Game Theory
• Economic Theory
• Decision Making
• +۳ مورد دیگر

Eran Shmaya is a Professor in the Department of Economics at Stony Brook University, located in the Social and Behavioral Sciences Building. His research focuses on game theory, economic theory, decision-making processes, and microeconomic systems. He is actively involved with the Stony Brook Center for Game Theory, contributing to foundational studies in strategic interactions and informational dynamics. Shmaya's work bridges theoretical economics with applied models, addressing topics such as Bayesian learning, optimal queueing regimes, and the behavioral implications of strategic miscalibration. His recent publications explore collective action problems, regret-minimization frameworks, and the structural analysis of optimal disclosure mechanisms. He teaches graduate-level courses in Microeconomics and Game Theory, reflecting his expertise in these areas. His research has been published in top-tier journals, with a focus on rigorous theoretical contributions and empirical relevance. Notable themes include the analysis of multi-agent systems, stochastic processes in economic contexts, and mechanisms for efficient resource allocation under uncertainty.

Jacqueline Messingمشاهده پروفایل

مدرس

• Language Revitalization
• Multilingualism
• Indigenous Communities
• +۴ مورد دیگر

Jacqueline Messing is a Lecturer in the Department of Anthropology at the University of Maryland (UMD) since 2010, specializing in sociolinguistics, ethnography, and education. She holds a PhD and MA in Anthropology from the University of Arizona and a BA in BDIC/Linguistics & Anthropology from the University of Massachusetts-Amherst. Research Interests: Her work focuses on language revitalization, multilingualism, and identity in Mexico and the U.S., particularly among indigenous communities. She examines how language policies, education systems, and sociocultural factors influence language shift and cultural preservation. Teaching: Courses include Educational Anthropology, Language and Identity, and Native American Languages. She actively engages students through innovative methods like Twitter hashtags for classroom discussions (#UMDLangIDRacism202X). Publications: Her research spans sociolinguistic studies in Tlaxcala, Mexico, and broader analyses of racism in language use. Key works include Language Acquisition, Shift, and Revitalization in Latin America (2016) and a tribute to linguist Jane H. Hill (2019). Advising/Grants: No formal advisee list or grants are mentioned in the provided materials. Her contributions are primarily through teaching and research.

Jeyakesavan Veerasamyمشاهده پروفایل

استاد

• Wireless Networks
• Algorithms
• Computer Science Education
• +۴ مورد دیگر

Dr. Jeyakesavan Veerasamy is a Professor of Instruction in Computer Science and Director of the Center for CS Education & Outreach at the Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. His roles include teaching computer science courses, directing outreach programs for K-12 students, and organizing summer research experiences and coding camps. He oversees initiatives such as cyber defense workshops, robotics, game design, and app development programs aimed at fostering computer science literacy in the Dallas area. Education: PhD in Computer Science, University of Texas at Dallas (1999) MS in Computer Science, University of Texas at Dallas (1993) B.E. in Electronics and Communication Engineering, Anna University (1990) Research focuses on wireless networking optimization, approximation algorithms, and computer science education. His work includes patents on wireless network efficiency and contributions to theoretical computer science, such as approximation algorithms for postman problems. He received the Best PhD Dissertation Award in Computer Science (1998-99) for his work on approximation algorithms. Outreach efforts include teacher training programs, professional development workshops, and partnerships with schools to deliver coding classes taught by UTD graduate students. His programs emphasize practical applications of computing through robotics, cybersecurity, and app development.

Dexin Shiمشاهده پروفایل

دانشیار

• Psychometrics
• Statistical Methodology
• Causal Inference
• +۳ مورد دیگر

Dr. Dexin Shi is an Associate Professor in the Department of Psychology at the University of South Carolina, affiliated with the McCausland College of Arts and Sciences. He holds expertise in quantitative psychology, focusing on statistical methodology development and application. Dr. Shi earned his Ph.D. in Quantitative Psychology from the University of Oklahoma (2016) and completed a postdoctoral fellowship at the University of South Carolina. His research emphasizes psychometrics, causal inference, missing data analysis, and model selection, with applications in educational, clinical, and health psychology. He has published over 50 peer-reviewed articles in top journals like Psychological Methods and Structural Equation Modeling . Notable awards include the 2021 Rising Star Award from the Association for Psychological Science and the McCausland Faculty Fellowship (2021–2024). Dr. Shi teaches courses such as PSYC 220 (Psychological Statistics) and advanced quantitative methods like PSYC 815 (Causal Inference). He has received grants for innovative pedagogy and open educational resources. His research trends highlight advancements in statistical techniques, including Bayesian methods, machine learning applications in equating, and causal pathway identification. He actively explores methodological challenges in psychological assessment and data analysis.

Carlos Borgesمشاهده پروفایل

دانشیار

• Inverse Scattering Problems
• Numerical Analysis
• Domain Decomposition
• +۴ مورد دیگر

Carlos Borges is an Associate Professor in the Department of Mathematics at the University of Central Florida (UCF), part of the College of Sciences. He holds a Ph.D. in Mathematics from Worcester Polytechnic Institute (2013) and has held postdoctoral positions at the Oden Institute (University of Texas-Austin) and the Courant Institute (NYU). His research focuses on inverse scattering problems, numerical analysis, and neural networks, with applications in computational engineering and mathematical physics. Education: PhD in Mathematics (Worcester Polytechnic Institute, 2013), MS in Mathematics (Instituto de Matematica Pura e Aplicada, 2007), BEng in Computer Engineering (Instituto Militar de Engenharia, 2002). Research interests include inverse scattering (acoustic, electromagnetic), domain decomposition methods, fast solvers, and machine learning integration in numerical analysis. His work emphasizes high-resolution parameter reconstruction and multi-frequency data utilization for obstacle identification. He advises four students: PhD candidates Isabela Vasconcellos Viana and Nick Arustamyan, and undergraduates Tina Tran and Eli Rivera-Sanchez. His recent publications (2023–2025) highlight advances in neural network-driven inverse scattering and robust algorithms for irregular geometries. He organizes the Scientific Computing Seminar at UCF, focusing on numerical analysis and computational methods. His software contributions include solvers for inverse scattering problems, available on GitHub. He is active in professional societies like SIAM and IEEE, and regularly presents at conferences such as SIAM CSE 2025.

Abey López-Garcíaمشاهده پروفایل

دانشیار

• Analysis
• Multiorthogonal Polynomials
• Potential Theory
• +۴ مورد دیگر

Abey López-García is an Associate Professor in the Department of Mathematics at the University of Central Florida (UCF). He joined UCF in August 2017 and has previously worked as a postdoc at the University of Leuven (2010-2013) and at the University of South Alabama (2013-2017). He holds a bachelor’s degree from Universidad Complutense de Madrid and a doctoral degree from Vanderbilt University. López-García is a collaborator of the GAMA research group at Universidad Carlos III de Madrid. Education : Bachelor in Mathematics, Universidad Complutense de Madrid, Spain PhD in Mathematics, Vanderbilt University His research focuses on Analysis, particularly multiorthogonal polynomials , minimal energy problems , potential theory , and connections to random matrices and random polynomials . His publications explore asymptotics, recurrence relations, and equilibrium problems in these areas. Recent work includes studies on Nikishin systems, greedy energy sequences, and lattice path models. Teaching : Courses include Calculus I-III, Ordinary Differential Equations, Linear Algebra, and advanced topics like Random Matrix Theory and Special Functions. Office hours are held on Mondays and Wednesdays. Collaborative ties with Universidad Carlos III de Madrid’s GAMA group and participation in international conferences (e.g., Constructive Functions 2025, Baylor Analysis Fest) highlight his academic engagements.

Peter Dologمشاهده پروفایل

دانشیار

• Recommender Systems
• Web Engineering
• Machine Learning
• +۵ مورد دیگر

Peter Dolog is an Associate Professor at the Department of Computer Science, Aalborg University, Denmark. He is affiliated with The Technical Faculty of IT and Design. His research focuses on Recommender Systems, Web Engineering, Machine Learning, and Health Informatics. He leads projects such as the MEco Medical Ecosystem and the IWIS Intelligent Web and Information Systems initiative. His work emphasizes explainable AI, knowledge graphs, and social media analytics for public health monitoring. Notable contributions include hypergraph-based recommendation models and sensitive information detection in legal documents. Projects include MEco (medical event surveillance) and IWIS (web systems personalization), funded by EU and national grants. His research spans 148 publications, with recent work on explainable recommendation systems and neural networks for medical data analysis. Collaborations include the University of Vienna and international conferences like ECIR and RecSys. Media coverage highlights his work on using social media for epidemic detection. Key research areas include recommendation systems with attention mechanisms, knowledge graph integration, and ethical AI applications in healthcare. His methodologies bridge machine learning with user-centric design, emphasizing transparency and explainability in AI-driven systems.

Jeff Edmondsمشاهده پروفایل

استاد

• Complexity Theory
• Scheduling
• Proof Systems
• +۴ مورد دیگر

Jeff Edmonds is a Professor in the Department of Electrical Engineering & Computer Science at York University. He received his PhD from the University of Toronto in 1992, with a thesis on time-space tradeoffs. His postdoctoral work at ICSI in Berkeley focused on secure data transmission for multimedia applications. He joined York University in 1995. His research interests include complexity theory, scheduling, proof systems, probability theory, combinatorics, and machine learning. He is affiliated with the Theory Group at York University and teaches advanced courses such as COSC6111 (Advanced Algorithm Design and Analysis), CSE3101 (Algorithms), and CSE2101 (Fundamentals of Data Structures). Edmonds has developed notable algorithms, including collaborative work with Kirk Pruhs on randomized cake-cutting protocols, which address fair division problems with applications beyond dessert, such as asset distribution and international negotiations. His teaching emphasizes foundational algorithmic techniques, including loop invariants, recursion, dynamic programming, and randomized algorithms. His research extends to network flow, linear programming, entropy, and computational complexity topics like NP-completeness and computability. He is also active in machine learning fundamentals, covering topics such as gradient descent and neural networks.

• Systems Engineering
• Control Engineering
• Discrete Event Systems
• +۷ مورد دیگر

Mariagrazia Dotoli is a Full Professor in Systems and Control Engineering at the Polytechnic University of Bari, Department of Electrical and Information Engineering, where she has been serving since 1999. She previously held the position of Vice Rector for Research (2011-2013) and served as a member elect of the Academic Senate (2012-2015). Currently, she coordinates the interuniversity PhD course in Industry 4.0 between the Polytechnic University of Bari and the University of Bari Aldo Moro. Her research interests span across multiple domains of systems engineering, with particular focus on discrete event industrial systems, Petri nets, manufacturing systems, supply chains, logistics and transportation systems, traffic networks, and energy systems. Her work bridges theoretical control systems with practical industrial applications, especially in the context of Industry 4.0 and smart manufacturing. Her extensive publication record demonstrates consistent contributions to automation science and engineering, with recent work focusing on warehouse optimization, collaborative robotics, supply chain management, and smart energy systems. Her research shows a clear trend toward integrating classical control theory with modern computational approaches, including machine learning and optimization algorithms for industrial applications. Prof. Dotoli maintains significant editorial responsibilities as Senior Editor of the IEEE Transactions on Automation Science and Engineering and Associate Editor for multiple IEEE journals. She has organized and chaired numerous international conferences including CASE2024, MED2021, and CODIT2020, demonstrating leadership in the automation community. Her academic career shows continuous progression from Assistant Professor (1999) to Full Professor, with additional leadership roles in university administration and international professional organizations. She remains actively engaged in both theoretical research and practical industrial applications of control systems engineering.

Saad Mneimnehمشاهده پروفایل

دانشیار

• Scheduling Algorithms
• Load Balancing
• Computational Biology
• +۴ مورد دیگر

Saad Mneimneh is an Associate Professor in the Department of Computer Science and the Department of Mathematics and Statistics at Hunter College, part of The City University of New York (CUNY). He holds a PhD in Computer Science from MIT and has held academic positions at Southern Methodist University and as a visiting scholar at various institutions. His research focuses on algorithmic techniques applied to scheduling, load balancing, and computational biology, particularly RNA interaction and protein secondary structure modeling. He has received awards such as the Cecil Insdorf Award for Excellence in Teaching and grants from NSF and PhRMA. Education: PhD in Computer Science from MIT (2002), SM and BE degrees from MIT and American University of Beirut, respectively. Professional roles include doctoral faculty at CUNY's Graduate Center and leadership in initiatives like CoSSMO. His teaching spans courses in discrete mathematics, algorithms, and computational biology. Research interests include network algorithms, combinatorial optimization, and interdisciplinary projects like the 'Counting with Code' educational initiative. He has supervised PhD students in computational biology and algorithm design. Grants include funding for RNA interaction studies and network analysis. Collaborations span bioinformatics, social network modeling, and mathematical pedagogy. Key contributions include work on RNA interaction algorithms, protein secondary structure models, and educational outreach. His work bridges theoretical computer science with applications in biology and mathematics education.

Eric J. Schmutzمشاهده پروفایل

استاد

• Discrete mathematics
• Probabilistic methods
• Combinatorial number theory

Eric J. Schmutz is a tenured Professor in the Department of Mathematics at Drexel University, actively contributing to combinatorial mathematics research and academic instruction. His career demonstrates sustained engagement in theoretical and applied probabilistic methods within discrete structures. His academic foundation includes: PhD in Mathematics from the University of Pennsylvania (1988) AB in Mathematics from Princeton University (1981) Professor Schmutz's research centers on discrete mathematics with specialized expertise in probabilistic combinatorics and combinatorial number theory . His work systematically explores asymptotic behaviors in random combinatorial structures, including permutation orders, partition statistics, and matrix properties. This interdisciplinary approach bridges pure mathematics with stochastic modeling, yielding insights applicable to computational complexity and algorithmic design. Analysis of his publication timeline reveals consistent contributions to combinatorial probability since the early 1990s, with recent work (2021) extending classical permutation theory. Key thematic threads include the statistical properties of random permutations, structural analysis of integer partitions, and number-theoretic functions—often involving collaborations with prominent mathematicians like Paul Erdős and William Goh. While no specific advising relationships or grant funding are documented in the source text, his extensive publication record spanning three decades implies significant mentorship of graduate researchers. The absence of laboratory or team affiliations suggests primarily theoretical work conducted through mathematical collaboration rather than experimental facilities.

• Algorithms
• Networks
• Parallel Computing
• +۳ مورد دیگر

Sandeep Bhatt is a Teaching Professor in the Department of Computer Science at Stevens Institute of Technology, within the Charles V. Schaefer, Jr. School of Engineering and Science. He holds a PhD, MS, and BS in Computer Science from MIT. His research focuses on Algorithms, Networks, Parallel Computing, and Theory of Computation, with recent contributions to Cybersecurity and Enterprise IT Risk Analysis. Dr. Bhatt's professional experience spans academia and industry, including roles as Director at Akamai Technologies, Bell Communications Research, and Yale University. He has contributed to numerous patents, such as hierarchical image segmentation and threat exchange security methods. Notable awards include the 2023 Outstanding Teacher Award from Stevens Alumni Association. His academic career emphasizes teaching and research in theoretical computer science, with courses like Algorithmic Complexity and Theory of Computation. His publications span cybersecurity frameworks, network analysis, and algorithmic advancements, reflecting a blend of theoretical rigor and practical industry application.